Longitudinal modelling of theory-based depressive vulnerabilities, depression trajectories and poor outcomes post-ACS

- We modeled theoretical vulnerabilities, depression trajectories and mortality.
- Vulnerability measures predicted subthreshold and persistent depression trajectories.
- Both depressive symptom trajectories predicted morbidity/mortality.
- Clinicians can use vulnerability measures to identify patients at risk of poor prognosis.

BackgroundDepression is associated with increased mortality in patients with acute coronary syndrome (ACS). However, little is known about the theoretical causes of depression trajectories post-ACS, and whether these trajectories predict subsequent morbidity/mortality. We tested a longitudinal model of depressive vulnerabilities, trajectories and mortality.MethodsA prospective observational study of 374 ACS patients was conducted. Participants completed questionnaires on theoretical vulnerabilities (interpersonal life events, reinforcing events, cognitive distortions, and Type D personality) during hospitalisation and depression at baseline and 3, 6 and 12 months post-hospitalisation. Latent class analysis determined trajectories of depression. Path analysis was used to test relationships among vulnerabilities, depression trajectories and outcomes (combination of 1-year morbidity and 7-year mortality).ResultsVulnerabilities independently predicted persistent and subthreshold depression trajectory categories, with effect sizes significantly highest for persistent depression. Both subthreshold and persistent depression trajectories were significant predictors of morbidity/mortality (e.g. persistent depression OR=2.4, 95% CI=1.8-3.1, relative to never depressed).LimitationsCausality cannot be inferred from these associations. We had no measures of history of depression or treatments, which may affect associations.ConclusionsTheoretical vulnerabilities predicted depression trajectories, which in turn predicted increased morbidity/mortality, demonstrating for the first time a potential longitudinal chain of events post-ACS. This longitudinal model has important practical implications as clinicians can use vulnerability measures to identify those at most risk of poor outcomes.